{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial for analyzing instrumental learning data with the `HDDMnnRL` module\n",
    "This is a tutorial for using the HDDMnnRL module to simultaneously estimate reinforcement learning parameters and decision parameters within a fully hierarchical bayesian estimation framework, including steps for sampling, assessing convergence, model fit, parameter recovery, and posterior predictive checks (model validation). The module uses the reinforcement learning sequential sampling model (RLSSM), a reinforcement learning model that replaces the standard “softmax” choice function with a sequential sampling process. The softmax and sequential sampling process is equivalent for capturing choice proportions, but the SSM also takes RT distributions into account. \n",
    "\n",
    "## OUTLINE ***\n",
    "[1. Background](#1.-Background) <br>\n",
    "[2. Installing the module](#2.-Installing-the-module) <br>\n",
    "[3. How the RLDDM works](#3.-How-the-RLDDM-works) <br>\n",
    "[4. Structuring data](#4.-Structuring-data) <br>\n",
    "[5. Running basic model](#5.-Running-basic-model) <br>\n",
    "[6. Checking results](#6.-Checking-results) <br>\n",
    "[7. Posterior predictive checks](#7.-Posterior-predictive-checks)<br>\n",
    "[8. Parameter recovery](#8.-Parameter-recovery)<br>\n",
    "[12. HDDMrlRegressor](#12.-HDDMrlRegressor)<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction to Reinforcement Learning - Sequential Sampling Models (RLSSM)\n",
    "\n",
    "Sequential sampling models (SSMs) are a powerful class of models used to summarize cognitive process dynamics underlying decision-making in various task settings. However, a large class of cognitive tasks in laboratory settings (eg. two-stage decision making tasks, grid-navigation tasks) or even otherwise, involve some sort of learning that results from the agent’s interactions with the environment. While SSMs can be used to model the decision processes, it cannot capture the learning dynamics involved in this class of tasks. Hence, reinforcement learning (RL) is a natural choice to model the learning processes in such settings. In reinforcement learning, researchers typically assume a static decision process such as a simple softmax choice rule (for probabilistic action selection), which is informed by some ‘utility’ (or ‘good-ness’) measure of taking a particular action in a given state. While reinforcement learning models can account for choice behaviors, they cannot capture the reaction time behaviors. \n",
    "\n",
    "To combine the strengths of sequential sampling models and reinforcement learning models, recent studies have used the drift diffusion model to jointly model choice and response time distributions during learning (for example, see [Pedersen, Frank & Biele (2017)](https://doi.org/10.3758/s13423-016-1199-y)). Such an approach allows researchers to study not only the across-trial dynamics of learning but the within-trial dynamics of choice processes, using a single model. The main idea behind these models is to allow a reinforcement learning process to drive the trial-by-trial parameters of a sequential sampling model (such as the basic drift diffusion model), to jointly capture reaction time and choice behavior in complex tasks which involve learning from feedback. As a result, RL-SSM is a much more powerful and expressive class of models. It naturally lends itself for use in computational modeling of numerous cognitive tasks where the 'learning process' informs the 'decision-making process'.\n",
    "\n",
    "For example, in case of the two-armed bandit task, the RLSSM estimates trial-by-trial drift rate as a scaled difference in expected rewards (expected reward for upper bound alternative minus expected reward for lower bound alternative). Expected rewards are updated with a delta learning rule such as Rescorla-Wagner update rule. The main idea here is that reward-based choices can be captured by an accumulation-to-bound process where drift rate is proportional to the difference in expected reward between options, and where expected rewards subsequently are updated in a trial-by-trial basis via reinforcement learning. <br><br>\n",
    "__drift rate on each trial depends on difference in expected rewards for the two alternatives (q_up and q_low):__ <br>\n",
    "drift rate = (q_up - q_low) * scaling <br><br>\n",
    "_where the scaling parameter describes the weight to put on the difference in q-values._<br><br>\n",
    "__expected reward (q) for chosen option is updated according to delta learning rule :__ <br>\n",
    "q_chosen = q_chosen + alpha * (feedback-q_chosen) <br><br>\n",
    "_where alpha weights the rate of learning on each trial._<br><br>\n",
    "So in principle all you need is the Wiener first passage time likelihood-function. The reason why HDDM is useful (and hence also `HDDMnnRL`) is that it automates the process of setting up and running your model, which tends to be very time consuming. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Installing the module\n",
    "The new version of HDDM (version 0.9.6) includes the LAN-based RLSSM implementation via `HDDMnnRL` and `HDDMnnRLRegressor` classes. You can install the new version of HDDM using the following command:\n",
    "\n",
    "    pip3 install git+https://github.com/hddm-devs/hddm\n",
    "\n",
    "Refer to [HDDM docs](https://hddm.readthedocs.io/en/latest/) for further installation instructions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. How the `HDDMnnRL` works (moving beyond the DDM)\n",
    "\n",
    "While SSM is a much broader class of models, including model variants which move beyond 2-choice scenarios, models which exchange the noise distribution in the diffusion, models which include attractor dynamics as well as models which deal with non-constant evidence criteria, RLSSM studies have been limited by the assumptions of DDM because of methodological convenience. The main reason being that DDM offers a closed-form analytical likelihood function (required for Bayesian inference for parameter estimation) unlike other SSMs. Therefore, the studies employ standard DDM even when other models can provide a better fit. \n",
    "\n",
    "We can use other SSMs but the lack of analytical likelihood demands use of likelihood-free inference methods such as ABC-MCMC. These inference methods are computationally expensive (and often require elaborate skills/experience), making the parameter estimation and model comparison significantly time-comsuming and effort-taking. In order to address these limitations in RLSSM settings, we leverage recently developed [Likelihood Approximation Networks (LANs)](https://doi.org/10.7554/eLife.65074) for fast, tractable and generalizable inference. LANs are neural networks that learn approximate empirical likelihoods for arbitrary generative models (in our case, SSMs) to faciliate fast posterior sampling during the parameter estimation process. Please refer to the paper for further information on LANs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Structuring data\n",
    "The HDDMnnRL module was created to make it easier to use a broader class of sequential sampling models in conjunction with reinforcement learning, to model instrumental learning data in a variety of task settings. If you are familiar with using HDDM, it shouldn't be a big step to start using HDDMnnRL. Please refresh your memory by starting with the [tutorial for HDDM](http://ski.clps.brown.edu/hddm_docs/index.html) first (especially critical if you have not used HDDM at all). A quick review of the HDDM [LAN extension](https://hddm.readthedocs.io/en/latest/lan_tutorial.html) would also help in understanding how the [Likelihood Approximation Networks](https://elifesciences.org/articles/65074), also used by the new HDDMnnRL class, are employed for fast, tractable inference for a variety of sequential sampling models. \n",
    "\n",
    "Running HDDMnnRL does require a few extra steps compared to HDDM, and because the model includes increased potential for parameter colinearity  (typically learning rate and the scaling parameter on drift rate) it is even more important to assess model fit, which will be covered below. Here are the most important steps for structuring your dataframe:\n",
    "1. Sort trials in ascending order within subject and condition, to ensure proper updating of expected rewards.\n",
    "2. Include a column called __'split_by'__ which identifies the different task conditions (__as integers__), to ensure reward updating will work properly for each condition without mixing values learned from one trial type to another. \n",
    "3. Code the response column with [stimulus-coding] (http://ski.clps.brown.edu/hddm_docs/howto.html#code-subject-responses). Although stimulus-coding and accuracy-coding often are the same in instrumental learning it is important that the upper and lower boundaries are represented by the same alternative within a condition, because expected rewards are linked to the thresholds/boundaries.\n",
    "4. __feedback__-column. This should be the reward received for the chosen option on each trial.\n",
    "5. __q_init__. Adjusting initial q-values is something that can improve model fit quite a bit. To allow the user to set their own initial values we therefore require that the dataframe includes a column called q_init. The function will set all initial q-values according to the first value in q_init. So this is not the most elegant method of allowing users to set inital value for expected rewards, but it works for now.\n",
    "\n",
    "#### Required columns in data:\n",
    "* __rt__: in seconds, same as in HDDM\n",
    "* __response__: 0 or 1. defines chosen stimulus, not accuracy.\n",
    "* __split_by__: needs to be an integer. Split_by defines conditions (trial-types) that should have the same variables (e.g. Q values) within subject: the trials need to be split by condition to ensure proper updating of expected rewards that do not bleed over into other trial-types. (e.g. if you have stimulus A and get reward you want that updated value to impact choice only for the next stimulus A trial but not necessarily the immediate trial afterwards, which may be of a different condition) \n",
    "* __subj_idx__: same as in HDDM, but even more important here because it is used to split trials\n",
    "* __feedback__: feedback on the current trial. can be any value.\n",
    "* __q_init__: used to initialize expected rewards. can be any value, but an unbiased initial value should be somewhere between the minimum and and maximum reward values (e.g. 0.5 for tasks with rewards of 0 and 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Running basic model\n",
    "To illustrate how to run the model we will use example data from the learning phase of the probabilistic selection task (PST). During the learning phase of the PST subjects choose between two  stimuli presented as Hiragana-letters (here represented as letters from the latin alphabet). There are three conditions with different probabilities of receiving reward (feedback=1) and non-reward (feedback=0). In the AB condition A is rewarded with 80% probability, B with 20%. In the CD condition C is rewarded with 70% probability and D with 30%, while in the EF condition E is rewarded with a 60% probability and F with 40%. The dataset is included in the data-folder in your installation of HDDM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated since IPython 4.0. You should import from ipyparallel instead.\n",
      "  \"You should import from ipyparallel instead.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import hddm\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc\n",
    "import kabuki\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "%matplotlib inline\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=np.VisibleDeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>response</th>\n",
       "      <th>cond</th>\n",
       "      <th>rt</th>\n",
       "      <th>trial</th>\n",
       "      <th>split_by</th>\n",
       "      <th>feedback</th>\n",
       "      <th>q_init</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CD</td>\n",
       "      <td>1.255</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>EF</td>\n",
       "      <td>0.778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AB</td>\n",
       "      <td>0.647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AB</td>\n",
       "      <td>0.750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EF</td>\n",
       "      <td>0.772</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subj_idx  response cond     rt  trial  split_by  feedback  q_init\n",
       "0        42       0.0   CD  1.255    1.0         1       0.0     0.5\n",
       "1        42       1.0   EF  0.778    1.0         2       0.0     0.5\n",
       "2        42       1.0   AB  0.647    1.0         0       1.0     0.5\n",
       "3        42       1.0   AB  0.750    2.0         0       1.0     0.5\n",
       "4        42       0.0   EF  0.772    2.0         2       1.0     0.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data. you will find this dataset in your hddm-folder under hddm/examples/rlddm_data.csv\n",
    "data = hddm.load_csv(\"rlddm_data.csv\")\n",
    "# check structure\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns in the datafile represent: __subj_idx__ (subject id), __response__ (1=best option), __cond__ (identifies condition, but not used in model), __rt__ (in seconds), 0=worst option), __trial__ (the trial-iteration for a subject within each condition), __split_by__ (identifying condition, used for running the model), __feedback__ (whether the response given was rewarded or not), __q_init__ (the initial q-value used for the model, explained above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 1500 of 1500 complete in 151.7 sec                    mean         std       2.5q        25q        50q        75q     97.5q       mc err\n",
      "a                1.75114   0.0837255    1.59815    1.69574    1.74802    1.80376   1.92983   0.00291067\n",
      "a_std            0.41232   0.0656654   0.308913   0.367279   0.405368   0.447927   0.56776   0.00289675\n",
      "a_subj.3         2.00788    0.102676    1.82598    1.93802    2.00144    2.07171   2.23154   0.00493845\n",
      "a_subj.4         1.89902   0.0536733     1.8001    1.86271    1.89701    1.93338   2.00713   0.00230884\n",
      "a_subj.5         1.48871   0.0715107    1.35642    1.43919     1.4863    1.53484   1.63991   0.00325001\n",
      "a_subj.6         2.40214   0.0937021    2.22399    2.33902    2.40306    2.46714   2.58429   0.00398112\n",
      "a_subj.8         2.50645    0.143083    2.24263    2.39808    2.50384    2.60774   2.78264   0.00698664\n",
      "a_subj.12        1.90226   0.0674087    1.77675     1.8556    1.89957    1.94815   2.04225   0.00320297\n",
      "a_subj.17        1.40972   0.0847752    1.22943    1.35608    1.40973     1.4647   1.58511   0.00491962\n",
      "a_subj.18        1.91654    0.124726    1.67828    1.83025    1.90977    1.99927   2.18669   0.00673609\n",
      "a_subj.19        2.08417   0.0794147    1.93045    2.03586    2.07986    2.13421   2.24956   0.00371623\n",
      "a_subj.20        1.71138   0.0843558    1.54864    1.65762    1.71167    1.76404   1.88942   0.00415579\n",
      "a_subj.22        1.19208   0.0324851    1.13197    1.17101    1.19101    1.21212   1.26011   0.00145357\n",
      "a_subj.23        1.93444    0.102446    1.74689    1.86325    1.92864    1.99758   2.15107   0.00503582\n",
      "a_subj.24        1.69065   0.0895549    1.53553    1.62691    1.68709     1.7483   1.87742    0.0040531\n",
      "a_subj.26        2.23017   0.0812178    2.08111    2.17227    2.22704     2.2827   2.39238   0.00361254\n",
      "a_subj.33        1.56584    0.069505    1.43544    1.52022    1.56458    1.61061   1.70839   0.00293951\n",
      "a_subj.34        1.82111   0.0925344    1.63987    1.75834    1.81963    1.88833   1.99193   0.00392129\n",
      "a_subj.35        1.83364   0.0910862    1.66166    1.77008    1.83076    1.89022   2.02515   0.00434047\n",
      "a_subj.36        1.32831   0.0711046    1.19866    1.27959    1.32398     1.3726    1.4815   0.00326779\n",
      "a_subj.39        1.57092   0.0570179    1.46831    1.53323    1.56933    1.60591   1.69108   0.00254816\n",
      "a_subj.42        1.72289   0.0780534    1.58344     1.6716    1.71808    1.77379    1.8881     0.003965\n",
      "a_subj.50        1.46373   0.0561168    1.36497    1.42376    1.46046    1.50046    1.5824   0.00367163\n",
      "a_subj.52        2.12277   0.0877004     1.9546    2.06291    2.11955    2.17823    2.3141   0.00422443\n",
      "a_subj.56        1.47434   0.0491814    1.38212    1.44175    1.47195    1.50692   1.57668   0.00236224\n",
      "a_subj.59        1.30969   0.0844833     1.1589    1.24941    1.31109    1.36849   1.48692   0.00618501\n",
      "a_subj.63        1.87185   0.0892341    1.71495    1.80829    1.86594    1.93234   2.06417   0.00389064\n",
      "a_subj.71        1.24574   0.0337506    1.18058    1.22069    1.24465     1.2673   1.31452   0.00144168\n",
      "a_subj.75       0.980626   0.0403033    0.90314   0.954133   0.978959    1.00467   1.06114   0.00181344\n",
      "a_subj.80        2.19842   0.0996843    2.00149    2.13232    2.19346    2.26375   2.39521   0.00447729\n",
      "v                4.15839    0.799926    2.70281    3.61434    4.11883    4.69384   5.82079    0.0512965\n",
      "v_std            3.17944    0.582619    2.24768    2.73327    3.12028    3.57136   4.51764    0.0380689\n",
      "v_subj.3         5.40933     2.40655    1.26099    3.71918    5.11575    6.79629   11.0188     0.186314\n",
      "v_subj.4        0.935868     0.13627   0.687918    0.84222   0.929464    1.02821   1.21449   0.00574671\n",
      "v_subj.5         5.53473     2.53813    1.29287    3.73108    5.17908    7.11548   11.2961     0.172848\n",
      "v_subj.6         5.07554     2.29577    1.82701    3.34519    4.68917    6.43253   10.5485     0.187447\n",
      "v_subj.8         3.19083      1.1531    1.76428    2.43081     2.9948    3.64642   6.33461     0.087577\n",
      "v_subj.12        1.80427    0.222639    1.40465    1.65858      1.796    1.93888   2.27892   0.00974675\n",
      "v_subj.17        7.71563     2.30556    3.88888    6.02563    7.55031    9.12299   12.7238     0.182644\n",
      "v_subj.18        6.87545     1.94651    3.83483    5.50429    6.56637    8.00345   11.3874      0.15955\n",
      "v_subj.19        11.8954     2.02558    8.68551    10.3678    11.7157    13.1091   16.4924     0.149489\n",
      "v_subj.20        4.80234     1.28635    2.96684    3.93358    4.52135    5.36143   8.30574     0.109046\n",
      "v_subj.22        2.21094    0.293277    1.62874    2.02192    2.20417    2.41537    2.7777    0.0132941\n",
      "v_subj.23         2.8458     1.98089   0.960886    1.48088    2.03871    3.77161   8.22177     0.174202\n",
      "v_subj.24        5.15582    0.764892    3.67864    4.64563    5.15715    5.65935   6.65519    0.0285252\n",
      "v_subj.26       0.539671     0.59661   0.254778   0.404612   0.480991   0.553289  0.877278    0.0407894\n",
      "v_subj.33        2.77334     2.13257   0.295247    1.13338    2.15707    4.00407   8.14233      0.12317\n",
      "v_subj.34        4.20132     2.39341    1.17239    2.25712    3.63896    5.72171   9.86739     0.177777\n",
      "v_subj.35        3.17444     1.95745    1.12509    1.74651    2.53013    4.17782   8.26337     0.165034\n",
      "v_subj.36        3.79907      2.4476    1.36971    2.15898    2.87502    4.67084   10.8683      0.21156\n",
      "v_subj.39        2.27064     1.97675   0.351198    0.93656    1.50589    3.11784   7.49375       0.1472\n",
      "v_subj.42        5.47322     2.08405    2.49925    3.95688    5.08889    6.58481   10.7829      0.18176\n",
      "v_subj.50        5.70079     2.01626    2.97595    4.19379    5.32492    6.75934   10.6905     0.167811\n",
      "v_subj.52        2.56559     1.27486    1.14649    1.72739    2.22896    3.00905   5.93314     0.103506\n",
      "v_subj.56        1.87343     1.94759   0.212418     0.6233    1.10997    2.31925   7.51623     0.148776\n",
      "v_subj.59        11.2906     1.94818    7.97466    9.83034    11.1653    12.6626   15.1351      0.14348\n",
      "v_subj.63        2.05387     1.17635   0.989297    1.45023    1.76468     2.2066   5.22357    0.0898511\n",
      "v_subj.71        2.32088     1.91716   0.634099    1.10631    1.53357     2.8856   7.62118     0.163783\n",
      "v_subj.75        3.76763    0.515318    2.81054    3.40891    3.73677    4.11781   4.78664    0.0226513\n",
      "v_subj.80        4.15577     2.33956   0.982954    2.46786    3.77248    5.33636   10.1362     0.175881\n",
      "t               0.434731   0.0449488   0.349777   0.402709   0.432116   0.465472  0.530516   0.00202517\n",
      "t_std           0.236392   0.0433431   0.169573   0.204342   0.230946   0.263389  0.335781   0.00225172\n",
      "t_subj.3         1.06328   0.0269397    1.00059    1.04824    1.06656    1.08227   1.10795   0.00125471\n",
      "t_subj.4        0.527271   0.0172907   0.490716   0.515989   0.527876    0.53992  0.557357  0.000658754\n",
      "t_subj.5        0.527646    0.014447   0.496233   0.518696   0.528208    0.53814  0.554243  0.000635292\n",
      "t_subj.6        0.397077   0.0299213   0.336127   0.377453   0.400033   0.418076  0.450114    0.0012808\n",
      "t_subj.8        0.658242   0.0471014   0.555228   0.629207   0.663679    0.69109  0.739203   0.00230504\n",
      "t_subj.12       0.409272   0.0138971   0.380402   0.399887   0.409665   0.419001  0.435401    0.0006559\n",
      "t_subj.17       0.498748   0.0187879   0.465868   0.489276   0.498427   0.505531  0.561609   0.00129853\n",
      "t_subj.18       0.434937   0.0272817   0.376619   0.418105   0.439607   0.455449  0.477842   0.00145894\n",
      "t_subj.19       0.419517   0.0147876   0.388266   0.410812   0.420257   0.429526  0.447385  0.000714105\n",
      "t_subj.20       0.516323   0.0130322    0.48754   0.508106   0.517514   0.524971  0.539053  0.000629322\n",
      "t_subj.22       0.335449  0.00512245   0.324519    0.33239   0.335702   0.338995   0.34542  0.000217804\n",
      "t_subj.23       0.481071   0.0256616   0.421632   0.467006   0.483806   0.498627  0.520986   0.00132232\n",
      "t_subj.24       0.453832   0.0134345   0.427156   0.445554   0.454765   0.463269  0.477105  0.000596106\n",
      "t_subj.26       0.444783   0.0328836   0.374369   0.423034   0.451583   0.469492  0.495256   0.00151257\n",
      "t_subj.33       0.195056    0.016176   0.162122   0.185433   0.195017   0.205708  0.226932  0.000650143\n",
      "t_subj.34       0.343165   0.0315926   0.271023   0.327582   0.343388   0.361161  0.412591   0.00137201\n",
      "t_subj.35        0.32677   0.0242391   0.277956   0.311416   0.326512   0.340882  0.378627   0.00110084\n",
      "t_subj.36       0.446884   0.0144441   0.412717   0.440513   0.450059   0.456836  0.467726  0.000633566\n",
      "t_subj.39       0.618878     0.01217   0.595016   0.611293   0.619984   0.626443  0.641677  0.000581635\n",
      "t_subj.42       0.393969   0.0156021   0.360875   0.385018   0.394406   0.405246  0.420589  0.000713536\n",
      "t_subj.50        0.52176    0.020651   0.482413    0.50144   0.530288   0.538845  0.548235   0.00142198\n",
      "t_subj.52       0.514314   0.0241081   0.464691   0.500631   0.515679   0.528825  0.562804    0.0010807\n",
      "t_subj.56       0.117367    0.010816  0.0938512   0.110853   0.118345   0.124671   0.13743  0.000534744\n",
      "t_subj.59       0.380717    0.023454   0.333847   0.363709   0.378302   0.403572  0.415476   0.00188964\n",
      "t_subj.63       0.476737   0.0209237   0.427639   0.465293   0.478463   0.490654  0.513883  0.000911725\n",
      "t_subj.71       0.162961  0.00548919   0.150952   0.159712   0.163301   0.166739  0.172795  0.000229584\n",
      "t_subj.75       0.260312  0.00688929   0.243917   0.257628   0.261829    0.26465   0.27016  0.000329039\n",
      "t_subj.80      0.0403546   0.0169699  0.0110397  0.0275679  0.0396382  0.0518146   0.07515  0.000796892\n",
      "alpha            -2.4752     0.38727   -3.27381   -2.72039   -2.46563   -2.22027  -1.73132     0.024173\n",
      "alpha_std        1.50582     0.28705    1.02513    1.30159    1.47813    1.67341   2.16245    0.0177882\n",
      "alpha_subj.3    -3.44582    0.761229   -4.50645   -3.90142    -3.5688   -3.16481   -1.1172     0.063098\n",
      "alpha_subj.4    0.237126    0.613602  -0.794058   -0.17478   0.175653   0.598227    1.7028    0.0264375\n",
      "alpha_subj.5    -3.40368    0.788364   -4.55092   -3.93851   -3.51397   -3.05458  -1.16324    0.0541566\n",
      "alpha_subj.6    -3.93563    0.597556   -4.97378   -4.34796    -4.0016   -3.55438  -2.62741    0.0500632\n",
      "alpha_subj.8     -2.3184    0.608761   -3.68071    -2.6901   -2.27008   -1.89455  -1.26035    0.0450248\n",
      "alpha_subj.12  -0.713202    0.387328   -1.51458  -0.951136  -0.720293  -0.466909    0.1033    0.0170274\n",
      "alpha_subj.17   -2.98879    0.456849   -3.78019   -3.28732   -3.03176   -2.73572  -1.96528    0.0365151\n",
      "alpha_subj.18   -2.91076    0.412397   -3.68444   -3.17673   -2.92772   -2.65501  -2.10694    0.0324668\n",
      "alpha_subj.19   -3.72053    0.228938   -4.18767   -3.86342   -3.71741   -3.56332  -3.28838    0.0162997\n",
      "alpha_subj.20   -2.40625    0.434508   -3.33223   -2.69369   -2.38142   -2.12126  -1.53731    0.0357525\n",
      "alpha_subj.22  -0.994157    0.211281   -1.39653   -1.14136  -0.989873  -0.846522 -0.569635   0.00958114\n",
      "alpha_subj.23   -2.44627     1.09817   -4.44185   -3.36588   -2.36064   -1.63009 -0.449431    0.0952876\n",
      "alpha_subj.24   -1.48682    0.224823    -1.9422   -1.62148   -1.48105   -1.34561   -1.0569    0.0074061\n",
      "alpha_subj.26   0.604183     1.36559   -4.01713   0.185604   0.726365    1.29128   2.58996    0.0988941\n",
      "alpha_subj.33   -3.23136     1.39147   -5.53349   -4.23992   -3.46426   -2.21955 -0.533487     0.086119\n",
      "alpha_subj.34    -3.0473     0.91459   -4.56504   -3.74049   -3.17223   -2.40633  -1.11687    0.0676703\n",
      "alpha_subj.35   -2.69845    0.991454   -4.42691   -3.51339   -2.71695   -1.94905  -0.79832     0.083924\n",
      "alpha_subj.36   -2.18601     1.08057   -4.18804   -3.01064   -2.13118    -1.3696 -0.219222    0.0888798\n",
      "alpha_subj.39   -3.19389     1.34091   -5.70502   -4.26935   -3.14082   -2.15396 -0.850496     0.101065\n",
      "alpha_subj.42    -3.2365     0.58383   -4.28719   -3.64026   -3.23312   -2.83965   -2.0334    0.0511965\n",
      "alpha_subj.50   -3.80546    0.669532   -4.96325   -4.25891   -3.86233   -3.43775  -2.36238    0.0550394\n",
      "alpha_subj.52   -2.98914    0.634666   -4.32377   -3.40425   -2.95264   -2.53491   -1.8269    0.0504978\n",
      "alpha_subj.56   -3.65603     1.46117   -6.20744   -4.73829   -3.64526   -2.63292 -0.808278     0.107188\n",
      "alpha_subj.59   -2.57749    0.239341   -3.02079   -2.73683   -2.58904   -2.41926  -2.10183    0.0153308\n",
      "alpha_subj.63   -1.87078    0.918292   -3.93444   -2.37009   -1.78878   -1.27689 -0.298987    0.0646095\n",
      "alpha_subj.71   -2.94137     1.59686   -5.55049   -4.28727   -2.76687   -1.96191  0.648818     0.127415\n",
      "alpha_subj.75   -1.41741    0.395315    -2.1895    -1.6736   -1.41537   -1.14351  -0.64445    0.0169447\n",
      "alpha_subj.80    -3.2501    0.804037   -4.57126   -3.78446   -3.33574   -2.83023  -1.42305    0.0617733\n",
      "DIC: 10122.571164\n",
      "deviance: 10056.119971\n",
      "pD: 66.451192\n"
     ]
    }
   ],
   "source": [
    "# run the model by calling hddm.HDDMrl (instead of hddm.HDDM for normal HDDM)\n",
    "m = hddm.HDDMrl(data)\n",
    "# set sample and burn-in\n",
    "m.sample(1500, burn=500, dbname=\"traces.db\", db=\"pickle\")\n",
    "# print stats to get an overview of posterior distribution of estimated parameters\n",
    "m.print_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Interpreting output from print_stats:__  <br>\n",
    "The model estimates group mean and standard deviation parameters and subject parameters for the following latent variables: <br>\n",
    "a = decision threshold <br>\n",
    "v = scaling parameter <br>\n",
    "t = non-decision time <br>\n",
    "alpha = learning rate, note that it's not bound between 0 and 1. to transform take inverse logit: np.exp(alpha)/(1+np.exp(alpha)) <br>\n",
    "The columns represent the mean, standard deviation and quantiles of the approximated posterior distribution of each parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDDMrl vs. HDDM\n",
    "__There are a few things to note that is different from the normal HDDM model.__ <br>\n",
    "First of all, the estimated learning rate does not necessarily fall between 0 and 1. This is because it is estimated as a normal distribution for purposes of sampling hierarchically and then transformed by an inverse logit function to 0<alpha<1. So to interpret alpha as learning rate you have to transform the samples in the trace back with np.exp(alpha)/(1+np.exp(alpha)). And if you estimate separate learning rates for positive and negative prediction errors ([see here](#9.-Separate-learning-rates-for-positive-and-negative-prediction-errors)) then you get learning rate for negative prediction errors with np.exp(alpha)/(1+np.exp(alpha)) and positive prediction errors with np.exp(pos_alpha)/(1+np.exp(pos_alpha)).<br>\n",
    "Second, the v-parameter in the output is the scaling factor that is multiplied by the difference in q-values, so it is not the actual drift rate (or rather, it is the equivalent drift rate when the difference in Q values is exactly 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Checking results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting a\n",
      "Plotting a_std\n",
      "Plotting v\n",
      "Plotting v_std\n",
      "Plotting t\n",
      "Plotting t_std\n",
      "Plotting alpha\n",
      "Plotting alpha_std\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the posteriors of parameters\n",
    "m.plot_posteriors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Fig__. The mixing of the posterior distribution and autocorrelation looks ok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convergence of chains\n",
    "The Gelman-Rubin statistic is a test of whether the chains in the model converges. The Gelman-Ruben statistic measures the degree of variation between and within chains. Values close to 1 indicate convergence and that there is small variation between chains, i.e. that they end up as the same distribution across chains. A common heuristic is to assume convergence if all values are below 1.1. To run this you need to run multiple models, combine them and perform the Gelman-Rubin statistic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 1500 of 1500 complete in 148.3 sec"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/kabuki/analyze.py:148: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  samples[i,:] = model.nodes_db.ix[name, 'node'].trace()\n",
      "/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:961: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  return getattr(section, self.name)[new_key]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'a': 1.0032460874018214,\n",
       " 'a_std': 1.0009841521872085,\n",
       " 'a_subj.3': 1.0004396186849591,\n",
       " 'a_subj.4': 0.9999534502455131,\n",
       " 'a_subj.5': 1.003486683386713,\n",
       " 'a_subj.6': 1.0019388857243001,\n",
       " 'a_subj.8': 1.0011512646603846,\n",
       " 'a_subj.12': 0.999566242847165,\n",
       " 'a_subj.17': 0.9997315595714608,\n",
       " 'a_subj.18': 1.007802746170568,\n",
       " 'a_subj.19': 1.0006648642694451,\n",
       " 'a_subj.20': 0.9997046168477743,\n",
       " 'a_subj.22': 0.999761021617672,\n",
       " 'a_subj.23': 0.999981103582067,\n",
       " 'a_subj.24': 0.9999604239014099,\n",
       " 'a_subj.26': 0.9999505203703547,\n",
       " 'a_subj.33': 1.0000641985256977,\n",
       " 'a_subj.34': 1.0019030857893532,\n",
       " 'a_subj.35': 0.9995619813519606,\n",
       " 'a_subj.36': 1.0009696684307878,\n",
       " 'a_subj.39': 1.0030366187047326,\n",
       " 'a_subj.42': 1.0004536365990904,\n",
       " 'a_subj.50': 1.0009246525621893,\n",
       " 'a_subj.52': 0.9998106391663311,\n",
       " 'a_subj.56': 1.002833217938155,\n",
       " 'a_subj.59': 1.0037497197540834,\n",
       " 'a_subj.63': 0.9999025733097772,\n",
       " 'a_subj.71': 1.0009484787096814,\n",
       " 'a_subj.75': 1.0130098962514307,\n",
       " 'a_subj.80': 0.9996362431035328,\n",
       " 'v': 1.0205809795803613,\n",
       " 'v_std': 1.017843838450935,\n",
       " 'v_subj.3': 1.0084966266683277,\n",
       " 'v_subj.4': 0.9995268898509083,\n",
       " 'v_subj.5': 1.0055342540841103,\n",
       " 'v_subj.6': 1.006091261867606,\n",
       " 'v_subj.8': 1.0363142858648078,\n",
       " 'v_subj.12': 0.9999768301313733,\n",
       " 'v_subj.17': 1.0071442953265444,\n",
       " 'v_subj.18': 1.0091310053160127,\n",
       " 'v_subj.19': 1.025227632557955,\n",
       " 'v_subj.20': 1.0047516808487722,\n",
       " 'v_subj.22': 0.9995696752962354,\n",
       " 'v_subj.23': 1.0207703297420343,\n",
       " 'v_subj.24': 1.0003032086685848,\n",
       " 'v_subj.26': 1.0063047977790485,\n",
       " 'v_subj.33': 1.007422885046991,\n",
       " 'v_subj.34': 1.0000055617828756,\n",
       " 'v_subj.35': 1.004080679982615,\n",
       " 'v_subj.36': 1.025998215179402,\n",
       " 'v_subj.39': 1.0176678234231622,\n",
       " 'v_subj.42': 1.0446533107136062,\n",
       " 'v_subj.50': 1.0162948205722397,\n",
       " 'v_subj.52': 1.0329846199571966,\n",
       " 'v_subj.56': 1.0187113333213196,\n",
       " 'v_subj.59': 1.0013982327012232,\n",
       " 'v_subj.63': 1.0084242116205158,\n",
       " 'v_subj.71': 1.0034722375822482,\n",
       " 'v_subj.75': 1.0011359012034806,\n",
       " 'v_subj.80': 1.0071658884593422,\n",
       " 't': 1.0005057563118305,\n",
       " 't_std': 1.0022540551935895,\n",
       " 't_subj.3': 1.000335111817265,\n",
       " 't_subj.4': 0.9995572720794353,\n",
       " 't_subj.5': 1.0010911542829217,\n",
       " 't_subj.6': 1.0005078503952383,\n",
       " 't_subj.8': 1.000937902509564,\n",
       " 't_subj.12': 1.0002256652751758,\n",
       " 't_subj.17': 1.0036774720777926,\n",
       " 't_subj.18': 1.0057241781128603,\n",
       " 't_subj.19': 1.000598016225987,\n",
       " 't_subj.20': 1.0000045946218536,\n",
       " 't_subj.22': 0.99983171259197,\n",
       " 't_subj.23': 1.0005420765808288,\n",
       " 't_subj.24': 1.0016080999110242,\n",
       " 't_subj.26': 0.999512841375442,\n",
       " 't_subj.33': 0.9998364286694233,\n",
       " 't_subj.34': 1.0000670998014844,\n",
       " 't_subj.35': 0.9996132338974929,\n",
       " 't_subj.36': 1.0042098480709771,\n",
       " 't_subj.39': 1.0006515809595558,\n",
       " 't_subj.42': 1.000771348791104,\n",
       " 't_subj.50': 1.002215744431939,\n",
       " 't_subj.52': 1.001052279977618,\n",
       " 't_subj.56': 1.0017299690798878,\n",
       " 't_subj.59': 1.0046305114183551,\n",
       " 't_subj.63': 0.9995008742263429,\n",
       " 't_subj.71': 1.001174121937735,\n",
       " 't_subj.75': 1.0153800235690629,\n",
       " 't_subj.80': 0.9999996479882802,\n",
       " 'alpha': 1.0117546794208931,\n",
       " 'alpha_std': 1.016548833490214,\n",
       " 'alpha_subj.3': 1.0095453823767975,\n",
       " 'alpha_subj.4': 0.9999967981599148,\n",
       " 'alpha_subj.5': 1.0013067894285956,\n",
       " 'alpha_subj.6': 1.0015822997094852,\n",
       " 'alpha_subj.8': 1.028833703352052,\n",
       " 'alpha_subj.12': 0.999682056083204,\n",
       " 'alpha_subj.17': 1.002551456868468,\n",
       " 'alpha_subj.18': 1.0128686930345914,\n",
       " 'alpha_subj.19': 1.0216644572624185,\n",
       " 'alpha_subj.20': 1.003469044949529,\n",
       " 'alpha_subj.22': 0.9998485992621597,\n",
       " 'alpha_subj.23': 1.0211627966678443,\n",
       " 'alpha_subj.24': 1.0029351365812929,\n",
       " 'alpha_subj.26': 1.0082171167684313,\n",
       " 'alpha_subj.33': 1.0088110869085494,\n",
       " 'alpha_subj.34': 1.000806268244852,\n",
       " 'alpha_subj.35': 0.9997657478647997,\n",
       " 'alpha_subj.36': 1.016349397314595,\n",
       " 'alpha_subj.39': 1.0154377853905259,\n",
       " 'alpha_subj.42': 1.0380447906771046,\n",
       " 'alpha_subj.50': 1.014307146980194,\n",
       " 'alpha_subj.52': 1.0421339866800452,\n",
       " 'alpha_subj.56': 1.0345554568486672,\n",
       " 'alpha_subj.59': 1.0004809998032806,\n",
       " 'alpha_subj.63': 1.0167696837574038,\n",
       " 'alpha_subj.71': 1.0030045474552403,\n",
       " 'alpha_subj.75': 1.000110789496688,\n",
       " 'alpha_subj.80': 1.0061715847351584}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate convergence\n",
    "from kabuki.analyze import gelman_rubin\n",
    "\n",
    "models = []\n",
    "for i in range(3):\n",
    "    m = hddm.HDDMrl(data=data)\n",
    "    m.sample(1500, burn=500, dbname=\"traces.db\", db=\"pickle\")\n",
    "    models.append(m)\n",
    "\n",
    "gelman_rubin(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0446533107136062"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(list(gelman_rubin(models).values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model seems to have converged, i.e. the Gelman-Rubin statistic is below 1.1 for all parameters. It is important to always run this test, especially for more complex models ([as with separate learning rates for positive and negative prediction errors](#9.-Separate-learning-rates-for-positive-and-negative-prediction-errors)). So now we can combine these three models to get a better approximation of the posterior distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the models we ran to test for convergence.\n",
    "m = kabuki.utils.concat_models(models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joint posterior distribution\n",
    "Another test of the model is to look at collinearity. If the estimation of parameters is very codependent (correlation is strong) it can indicate that their variance trades off, in particular if there is a negative correlation. The following plot shows there is generally low correlation across all combinations of parameters. It does not seem to be the case for this dataset, but common for RLDDM is a negative correlation between learning rate and the scaling factor, similar to what's usually observed between learning rate and inverse temperature for RL models that uses softmax as the choice rule (e.g. [Daw, 2011](https://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199600434.001.0001/acprof-9780199600434-chapter-001))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha, t, a, v = m.nodes_db.node[[\"alpha\", \"t\", \"a\", \"v\"]]\n",
    "samples = {\"alpha\": alpha.trace(), \"t\": t.trace(), \"a\": a.trace(), \"v\": v.trace()}\n",
    "samp = pd.DataFrame(data=samples)\n",
    "\n",
    "\n",
    "def corrfunc(x, y, **kws):\n",
    "    r, _ = stats.pearsonr(x, y)\n",
    "    ax = plt.gca()\n",
    "    ax.annotate(\"r = {:.2f}\".format(r), xy=(0.1, 0.9), xycoords=ax.transAxes)\n",
    "\n",
    "\n",
    "g = sns.PairGrid(samp, palette=[\"red\"])\n",
    "g.map_upper(plt.scatter, s=10)\n",
    "g.map_diag(sns.distplot, kde=False)\n",
    "g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n",
    "g.map_lower(corrfunc)\n",
    "g.savefig(\"matrix_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Posterior predictive checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important test of the model is its ability to recreate the observed data. This can be tested with posterior predictive checks, which involves simulating data using estimated parameters and comparing observed and simulated results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### extract traces\n",
    "The first step then is to extract the traces from the estimated model. The function get_traces() gives you the samples (row) from the approaximated posterior distribution for all of the estimated group and subject parameters (column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>a_std</th>\n",
       "      <th>a_subj.3</th>\n",
       "      <th>a_subj.4</th>\n",
       "      <th>a_subj.5</th>\n",
       "      <th>a_subj.6</th>\n",
       "      <th>a_subj.8</th>\n",
       "      <th>a_subj.12</th>\n",
       "      <th>a_subj.17</th>\n",
       "      <th>a_subj.18</th>\n",
       "      <th>...</th>\n",
       "      <th>alpha_subj.39</th>\n",
       "      <th>alpha_subj.42</th>\n",
       "      <th>alpha_subj.50</th>\n",
       "      <th>alpha_subj.52</th>\n",
       "      <th>alpha_subj.56</th>\n",
       "      <th>alpha_subj.59</th>\n",
       "      <th>alpha_subj.63</th>\n",
       "      <th>alpha_subj.71</th>\n",
       "      <th>alpha_subj.75</th>\n",
       "      <th>alpha_subj.80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.663258</td>\n",
       "      <td>0.379335</td>\n",
       "      <td>1.999766</td>\n",
       "      <td>1.964781</td>\n",
       "      <td>1.501228</td>\n",
       "      <td>2.558870</td>\n",
       "      <td>2.134354</td>\n",
       "      <td>1.808065</td>\n",
       "      <td>1.428145</td>\n",
       "      <td>1.904139</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.569240</td>\n",
       "      <td>-2.348125</td>\n",
       "      <td>-2.279293</td>\n",
       "      <td>-3.002542</td>\n",
       "      <td>-2.038603</td>\n",
       "      <td>-2.255088</td>\n",
       "      <td>-0.153830</td>\n",
       "      <td>-1.809325</td>\n",
       "      <td>-1.738580</td>\n",
       "      <td>-2.323516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.623170</td>\n",
       "      <td>0.359708</td>\n",
       "      <td>1.912802</td>\n",
       "      <td>1.967012</td>\n",
       "      <td>1.462844</td>\n",
       "      <td>2.466133</td>\n",
       "      <td>2.347265</td>\n",
       "      <td>2.031387</td>\n",
       "      <td>1.476833</td>\n",
       "      <td>1.883079</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.593007</td>\n",
       "      <td>-2.564579</td>\n",
       "      <td>-2.787299</td>\n",
       "      <td>-2.817155</td>\n",
       "      <td>0.128265</td>\n",
       "      <td>-2.358720</td>\n",
       "      <td>-0.709526</td>\n",
       "      <td>-1.876886</td>\n",
       "      <td>-1.428454</td>\n",
       "      <td>-3.140597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.817655</td>\n",
       "      <td>0.312626</td>\n",
       "      <td>2.013651</td>\n",
       "      <td>1.870517</td>\n",
       "      <td>1.438784</td>\n",
       "      <td>2.332917</td>\n",
       "      <td>2.426746</td>\n",
       "      <td>2.079006</td>\n",
       "      <td>1.264283</td>\n",
       "      <td>1.939135</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.187908</td>\n",
       "      <td>-2.566549</td>\n",
       "      <td>-3.341771</td>\n",
       "      <td>-3.206621</td>\n",
       "      <td>-0.724311</td>\n",
       "      <td>-2.446694</td>\n",
       "      <td>-1.133453</td>\n",
       "      <td>-2.153231</td>\n",
       "      <td>-1.589570</td>\n",
       "      <td>-2.702218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.762559</td>\n",
       "      <td>0.573961</td>\n",
       "      <td>1.852805</td>\n",
       "      <td>1.920585</td>\n",
       "      <td>1.456088</td>\n",
       "      <td>2.437470</td>\n",
       "      <td>2.679242</td>\n",
       "      <td>2.099067</td>\n",
       "      <td>1.311264</td>\n",
       "      <td>1.902507</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.045972</td>\n",
       "      <td>-2.466571</td>\n",
       "      <td>-3.093191</td>\n",
       "      <td>-3.204751</td>\n",
       "      <td>-3.220443</td>\n",
       "      <td>-2.381405</td>\n",
       "      <td>-1.060397</td>\n",
       "      <td>-1.521510</td>\n",
       "      <td>-1.892220</td>\n",
       "      <td>-2.902676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.725824</td>\n",
       "      <td>0.472488</td>\n",
       "      <td>1.907957</td>\n",
       "      <td>1.954045</td>\n",
       "      <td>1.462033</td>\n",
       "      <td>2.394734</td>\n",
       "      <td>2.389626</td>\n",
       "      <td>1.928428</td>\n",
       "      <td>1.334218</td>\n",
       "      <td>1.790217</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.035124</td>\n",
       "      <td>-2.679132</td>\n",
       "      <td>-3.821553</td>\n",
       "      <td>-3.372584</td>\n",
       "      <td>-1.139438</td>\n",
       "      <td>-2.372234</td>\n",
       "      <td>-0.895417</td>\n",
       "      <td>-1.900813</td>\n",
       "      <td>-2.196233</td>\n",
       "      <td>-3.063793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          a     a_std  a_subj.3  a_subj.4  a_subj.5  a_subj.6  a_subj.8  \\\n",
       "0  1.663258  0.379335  1.999766  1.964781  1.501228  2.558870  2.134354   \n",
       "1  1.623170  0.359708  1.912802  1.967012  1.462844  2.466133  2.347265   \n",
       "2  1.817655  0.312626  2.013651  1.870517  1.438784  2.332917  2.426746   \n",
       "3  1.762559  0.573961  1.852805  1.920585  1.456088  2.437470  2.679242   \n",
       "4  1.725824  0.472488  1.907957  1.954045  1.462033  2.394734  2.389626   \n",
       "\n",
       "   a_subj.12  a_subj.17  a_subj.18  ...  alpha_subj.39  alpha_subj.42  \\\n",
       "0   1.808065   1.428145   1.904139  ...      -2.569240      -2.348125   \n",
       "1   2.031387   1.476833   1.883079  ...      -2.593007      -2.564579   \n",
       "2   2.079006   1.264283   1.939135  ...      -3.187908      -2.566549   \n",
       "3   2.099067   1.311264   1.902507  ...      -2.045972      -2.466571   \n",
       "4   1.928428   1.334218   1.790217  ...      -2.035124      -2.679132   \n",
       "\n",
       "   alpha_subj.50  alpha_subj.52  alpha_subj.56  alpha_subj.59  alpha_subj.63  \\\n",
       "0      -2.279293      -3.002542      -2.038603      -2.255088      -0.153830   \n",
       "1      -2.787299      -2.817155       0.128265      -2.358720      -0.709526   \n",
       "2      -3.341771      -3.206621      -0.724311      -2.446694      -1.133453   \n",
       "3      -3.093191      -3.204751      -3.220443      -2.381405      -1.060397   \n",
       "4      -3.821553      -3.372584      -1.139438      -2.372234      -0.895417   \n",
       "\n",
       "   alpha_subj.71  alpha_subj.75  alpha_subj.80  \n",
       "0      -1.809325      -1.738580      -2.323516  \n",
       "1      -1.876886      -1.428454      -3.140597  \n",
       "2      -2.153231      -1.589570      -2.702218  \n",
       "3      -1.521510      -1.892220      -2.902676  \n",
       "4      -1.900813      -2.196233      -3.063793  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traces = m.get_traces()\n",
    "traces.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simulating data\n",
    "__Now that we have the traces the next step is to simulate data using the estimated parameters. The RLDDM includes a function to simulate data. Here's an example of how to use the simulation-function for RLDDM. This example explains how to generate data with binary outcomes. See [here](#11.-Probabilistic-binary-outcomes-vs.-normally-distributed-outcomes) for an example on simulating data with normally distributed outcomes. Inputs to function: <br>\n",
    "a__ = decision threshold <br>\n",
    "**t** = non-decision time <br>\n",
    "__alpha__ = learning rate <br>\n",
    "__pos_alpha__ = defaults to 0. if given it defines the learning rate for positive prediction errors. alpha then becomes the learning rate_ for negative prediction errors.  <br>\n",
    "__scaler__ = the scaling factor that is multiplied with the difference in q-values to calculate trial-by-trial drift rate <br>\n",
    "__p_upper__ = the probability of reward for the option represented by the upper boundary. The current version thus only works for outcomes that are either 1 or 0 <br>\n",
    "__p_lower__ = the probability of reward for the option represented by the lower boundary. <br>\n",
    "__subjs__ = number of subjects to simulate data for. <br>\n",
    "__split_by__ = define the condition which makes it easier to append data from different conditions. <br>\n",
    "__size__ = number of trials per subject. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>q_up</th>\n",
       "      <th>q_low</th>\n",
       "      <th>sim_drift</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>feedback</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>split_by</th>\n",
       "      <th>trial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>0.56000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.45600</td>\n",
       "      <td>0.28800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.564</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.45600</td>\n",
       "      <td>0.44800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.45600</td>\n",
       "      <td>0.57600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.36480</td>\n",
       "      <td>0.75840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.29184</td>\n",
       "      <td>0.90432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.7952</td>\n",
       "      <td>0.29184</td>\n",
       "      <td>1.00672</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     q_up    q_low  sim_drift  response     rt  feedback  subj_idx  split_by  \\\n",
       "0  0.5000  0.50000    0.00000       1.0  0.770       1.0         0         0   \n",
       "1  0.6000  0.50000    0.20000       0.0  0.403       0.0         0         0   \n",
       "2  0.6000  0.40000    0.40000       0.0  0.612       0.0         0         0   \n",
       "3  0.6000  0.32000    0.56000       0.0  0.404       1.0         0         0   \n",
       "4  0.6000  0.45600    0.28800       1.0  0.564       1.0         0         0   \n",
       "5  0.6800  0.45600    0.44800       1.0  0.416       1.0         0         0   \n",
       "6  0.7440  0.45600    0.57600       0.0  0.430       0.0         0         0   \n",
       "7  0.7440  0.36480    0.75840       0.0  0.409       0.0         0         0   \n",
       "8  0.7440  0.29184    0.90432       1.0  0.361       1.0         0         0   \n",
       "9  0.7952  0.29184    1.00672       1.0  0.537       0.0         0         0   \n",
       "\n",
       "   trial  \n",
       "0      1  \n",
       "1      2  \n",
       "2      3  \n",
       "3      4  \n",
       "4      5  \n",
       "5      6  \n",
       "6      7  \n",
       "7      8  \n",
       "8      9  \n",
       "9     10  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddm.generate.gen_rand_rlddm_data(\n",
    "    a=1,\n",
    "    t=0.3,\n",
    "    alpha=0.2,\n",
    "    scaler=2,\n",
    "    p_upper=0.8,\n",
    "    p_lower=0.2,\n",
    "    subjs=1,\n",
    "    split_by=0,\n",
    "    size=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__How to interpret columns in the resulting dataframe__ <br>\n",
    "__q_up__ = expected reward for option represented by upper boundary <br>\n",
    "__q_low__ = expected reward for option represented by lower boundary <br>\n",
    "__sim_drift__ = the drift rate for each trial calculated as: (q_up-q_low)*scaler <br>\n",
    "__response__ = simulated choice <br>\n",
    "__rt__ = simulated response time <br>\n",
    "__feedback__ = observed feedback for chosen option <br>\n",
    "__subj_idx__ = subject id (starts at 0) <br>\n",
    "__split_by__ = condition as integer <br>\n",
    "__trial__ = current trial (starts at 1) <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate data with estimated parameter values and compare to observed data\n",
    "Now that we know how to extract traces and simulate data we can combine this to create a dataset similar to our observed data. This process is currently not automated but the following is an example code using the dataset we analyzed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [31:09<00:00, 37.39s/it]\n",
      "/Users/madslundpedersen/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/frame.py:7138: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort,\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm  # progress tracker\n",
    "\n",
    "# create empty dataframe to store simulated data\n",
    "sim_data = pd.DataFrame()\n",
    "# create a column samp to be used to identify the simulated data sets\n",
    "data[\"samp\"] = 0\n",
    "# load traces\n",
    "traces = m.get_traces()\n",
    "# decide how many times to repeat simulation process. repeating this multiple times is generally recommended,\n",
    "# as it better captures the uncertainty in the posterior distribution, but will also take some time\n",
    "for i in tqdm(range(1, 51)):\n",
    "    # randomly select a row in the traces to use for extracting parameter values\n",
    "    sample = np.random.randint(0, traces.shape[0] - 1)\n",
    "    # loop through all subjects in observed data\n",
    "    for s in data.subj_idx.unique():\n",
    "        # get number of trials for each condition.\n",
    "        size0 = len(\n",
    "            data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 0)].trial.unique()\n",
    "        )\n",
    "        size1 = len(\n",
    "            data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 1)].trial.unique()\n",
    "        )\n",
    "        size2 = len(\n",
    "            data[(data[\"subj_idx\"] == s) & (data[\"split_by\"] == 2)].trial.unique()\n",
    "        )\n",
    "        # set parameter values for simulation\n",
    "        a = traces.loc[sample, \"a_subj.\" + str(s)]\n",
    "        t = traces.loc[sample, \"t_subj.\" + str(s)]\n",
    "        scaler = traces.loc[sample, \"v_subj.\" + str(s)]\n",
    "        alphaInv = traces.loc[sample, \"alpha_subj.\" + str(s)]\n",
    "        # take inverse logit of estimated alpha\n",
    "        alpha = np.exp(alphaInv) / (1 + np.exp(alphaInv))\n",
    "        # simulate data for each condition changing only values of size, p_upper, p_lower and split_by between conditions.\n",
    "        sim_data0 = hddm.generate.gen_rand_rlddm_data(\n",
    "            a=a,\n",
    "            t=t,\n",
    "            scaler=scaler,\n",
    "            alpha=alpha,\n",
    "            size=size0,\n",
    "            p_upper=0.8,\n",
    "            p_lower=0.2,\n",
    "            split_by=0,\n",
    "        )\n",
    "        sim_data1 = hddm.generate.gen_rand_rlddm_data(\n",
    "            a=a,\n",
    "            t=t,\n",
    "            scaler=scaler,\n",
    "            alpha=alpha,\n",
    "            size=size1,\n",
    "            p_upper=0.7,\n",
    "            p_lower=0.3,\n",
    "            split_by=1,\n",
    "        )\n",
    "        sim_data2 = hddm.generate.gen_rand_rlddm_data(\n",
    "            a=a,\n",
    "            t=t,\n",
    "            scaler=scaler,\n",
    "            alpha=alpha,\n",
    "            size=size2,\n",
    "            p_upper=0.6,\n",
    "            p_lower=0.4,\n",
    "            split_by=2,\n",
    "        )\n",
    "        # append the conditions\n",
    "        sim_data0 = sim_data0.append([sim_data1, sim_data2], ignore_index=True)\n",
    "        # assign subj_idx\n",
    "        sim_data0[\"subj_idx\"] = s\n",
    "        # identify that these are simulated data\n",
    "        sim_data0[\"type\"] = \"simulated\"\n",
    "        # identify the simulated data\n",
    "        sim_data0[\"samp\"] = i\n",
    "        # append data from each subject\n",
    "        sim_data = sim_data.append(sim_data0, ignore_index=True)\n",
    "# combine observed and simulated data\n",
    "ppc_data = data[\n",
    "    [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"samp\"]\n",
    "].copy()\n",
    "ppc_data[\"type\"] = \"observed\"\n",
    "ppc_sdata = sim_data[\n",
    "    [\"subj_idx\", \"response\", \"split_by\", \"rt\", \"trial\", \"feedback\", \"type\", \"samp\"]\n",
    "].copy()\n",
    "ppc_data = ppc_data.append(ppc_sdata)\n",
    "ppc_data.to_csv(\"ppc_data_tutorial.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "Now that we have a dataframe with both observed and simulated data we can plot to see whether the simulated data are able to capture observed choice and reaction times. To capture the uncertainty in the simulated data we want to identify how much choice and reaction differs across the simulated data sets. A good measure of this is to calculate the highest posterior density/highest density interval for summary scores of the generated data. Below we calculate highest posterior density with an alpha set to 0.1, which means that we are describing the range of the 90% most likely values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for practical reasons we only look at the first 40 trials for each subject in a given condition\n",
    "plot_ppc_data = ppc_data[ppc_data.trial < 41].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin trials to for smoother estimate of response proportion across learning\n",
    "plot_ppc_data[\"bin_trial\"] = pd.cut(\n",
    "    plot_ppc_data.trial, 11, labels=np.linspace(0, 10, 11)\n",
    ").astype(\"int64\")\n",
    "# calculate means for each sample\n",
    "sums = (\n",
    "    plot_ppc_data.groupby([\"bin_trial\", \"split_by\", \"samp\", \"type\"])\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "# calculate the overall mean response across samples\n",
    "ppc_sim = sums.groupby([\"bin_trial\", \"split_by\", \"type\"]).mean().reset_index()\n",
    "# initiate columns that will have the upper and lower bound of the hpd\n",
    "ppc_sim[\"upper_hpd\"] = 0\n",
    "ppc_sim[\"lower_hpd\"] = 0\n",
    "for i in range(0, ppc_sim.shape[0]):\n",
    "    # calculate the hpd/hdi of the predicted mean responses across bin_trials\n",
    "    hdi = pymc.utils.hpd(\n",
    "        sums.response[\n",
    "            (sums[\"bin_trial\"] == ppc_sim.bin_trial[i])\n",
    "            & (sums[\"split_by\"] == ppc_sim.split_by[i])\n",
    "            & (sums[\"type\"] == ppc_sim.type[i])\n",
    "        ],\n",
    "        alpha=0.1,\n",
    "    )\n",
    "    ppc_sim.loc[i, \"upper_hpd\"] = hdi[1]\n",
    "    ppc_sim.loc[i, \"lower_hpd\"] = hdi[0]\n",
    "# calculate error term as the distance from upper bound to mean\n",
    "ppc_sim[\"up_err\"] = ppc_sim[\"upper_hpd\"] - ppc_sim[\"response\"]\n",
    "ppc_sim[\"low_err\"] = ppc_sim[\"response\"] - ppc_sim[\"lower_hpd\"]\n",
    "ppc_sim[\"model\"] = \"RLDDM_single_learning\"\n",
    "ppc_sim.to_csv(\"ppc_choicedata_tutorial.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting evolution of choice proportion for best option across learning for observed and simulated data.\n",
    "fig, axs = plt.subplots(figsize=(15, 5), nrows=1, ncols=3, sharex=True, sharey=True)\n",
    "for i in range(0, 3):\n",
    "    ax = axs[i]\n",
    "    d = ppc_sim[(ppc_sim.split_by == i) & (ppc_sim.type == \"simulated\")]\n",
    "    ax.errorbar(\n",
    "        d.bin_trial,\n",
    "        d.response,\n",
    "        yerr=[d.low_err, d.up_err],\n",
    "        label=\"simulated\",\n",
    "        color=\"orange\",\n",
    "    )\n",
    "    d = ppc_sim[(ppc_sim.split_by == i) & (ppc_sim.type == \"observed\")]\n",
    "    ax.plot(d.bin_trial, d.response, linewidth=3, label=\"observed\")\n",
    "    ax.set_title(\"split_by = %i\" % i, fontsize=20)\n",
    "    ax.set_ylabel(\"mean response\")\n",
    "    ax.set_xlabel(\"trial\")\n",
    "plt.legend()\n",
    "fig.savefig(\"PPCchoice.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Fig.__ The plots display the rate of choosing the best option (response = 1) across learning and condition. The model generates data (orange) that closely follows the observed behavior (blue), with the exception of overpredicting performance early in the most difficult condition (split_by=2). Uncertainty in the generated data is captured by the 90% highest density interval of the means across simulated datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 734.85x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set reaction time to be negative for lower bound responses (response=0)\n",
    "plot_ppc_data[\"reaction time\"] = np.where(\n",
    "    plot_ppc_data[\"response\"] == 1, plot_ppc_data.rt, 0 - plot_ppc_data.rt\n",
    ")\n",
    "# plotting evolution of choice proportion for best option across learning for observed and simulated data. We use bins of trials because plotting individual trials would be very noisy.\n",
    "g = sns.FacetGrid(plot_ppc_data, col=\"split_by\", hue=\"type\")\n",
    "g.map(sns.kdeplot, \"reaction time\", bw=0.05).set_ylabels(\"Density\")\n",
    "g.add_legend()\n",
    "g.savefig(\"PPCrt_dist.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Fig.__ Density plots of observed and predicted reaction time across conditions. RTs for lower boundary choices (i.e. worst option choices) are set to be negative (0-RT) to be able to separate upper and lower bound responses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Parameter recovery\n",
    "To validate the RLDDM we ran a parameter recovery study to test to which degree the model can recover the parameter values used to simulate data. To do this we generated 81 synthetic datasets with 50 subjects performing 70 trials each. The 81 datasets were simulated using all combinations of three plausible parameter values for decision threshold, non-decision time, learning rate and the scaling parameter onto drift rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimated values split by simulated vales \n",
    "We can plot simulated together with the estimated values to test the models ability to recover parameters, and to see if there are any values that are more difficult to recover than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_recovery = hddm.load_csv(\"recovery_sim_est_rlddm.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"a\", y=\"e_a\", data=param_recovery, palette=\"Set1\")\n",
    "g.set_axis_labels(\"Simulated threshold\", \"Estimated threshold\")\n",
    "plt.title(\"Decision threshold\")\n",
    "g.savefig(\"Threshold_recovery.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"alpha\", y=\"e_alphaT\", data=param_recovery, palette=\"Set1\")\n",
    "g.set_axis_labels(\"Simulated alpha\", \"Estimated alpha\")\n",
    "plt.title(\"Learning rate\")\n",
    "g.savefig(\"Alpha_recovery.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"scaler\", y=\"e_v\", data=param_recovery, palette=\"Set1\")\n",
    "g.set_axis_labels(\"Simulated scaling\", \"Estimated scaling\")\n",
    "plt.title(\"Scaling drift rate\")\n",
    "g.savefig(\"Scaler_recovery.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"t\", y=\"e_t\", data=param_recovery, palette=\"Set1\")\n",
    "g.set_axis_labels(\"Simulated NDT\", \"Estimated NDT\")\n",
    "plt.title(\"Non-decision time\")\n",
    "g.savefig(\"NDT_recovery.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Fig.__ The correlation between simulated and estimated parameter values are high, which means recovery is good. There is somewhat worse recovery for the learning rate and scaling parameter, which makes sense given that they to a degree can explain the same variance (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. HDDMrlRegressor\n",
    "\n",
    "As of version 0.7.6. HDDM includes a module for estimating the impact of continuous regressor onto RLDDM parameters. The module, called HDDMrlRegressor, works the same way as the HDDMRegressor for the normal DDM. The method allows estimation of the association of e.g. neural measures onto parameters. To illustrate the method we extend the function to generate rlddm_data by adding a normally distributed regressor and including a coefficient called 'neural'.Note that to run the HDDMrlRegressor you need to include alpha when specifying the model. For more information on how to set up regressor models look at the tutorial for HDDM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to generate rlddm-data that adds a neural regressor to decision threshold\n",
    "def gen_rand_reg_rlddm_data(\n",
    "    a,\n",
    "    t,\n",
    "    scaler,\n",
    "    alpha,\n",
    "    neural,\n",
    "    size=1,\n",
    "    p_upper=1,\n",
    "    p_lower=0,\n",
    "    z=0.5,\n",
    "    q_init=0.5,\n",
    "    split_by=0,\n",
    "    subjs=1,\n",
    "):\n",
    "    all_data = []\n",
    "    n = size\n",
    "    # set sd for variables to generate subject-parameters from group distribution\n",
    "    sd_t = 0.02\n",
    "    sd_a = 0.1\n",
    "    sd_alpha = 0.1\n",
    "    sd_v = 0.25\n",
    "    # save parameter values as group-values\n",
    "    tg = t\n",
    "    ag = a\n",
    "    alphag = alpha\n",
    "    scalerg = scaler\n",
    "    for s in range(0, subjs):\n",
    "        t = (\n",
    "            np.maximum(0.05, np.random.normal(loc=tg, scale=sd_t, size=1))\n",
    "            if subjs > 1\n",
    "            else tg\n",
    "        )\n",
    "        a = (\n",
    "            np.maximum(0.05, np.random.normal(loc=ag, scale=sd_a, size=1))\n",
    "            if subjs > 1\n",
    "            else ag\n",
    "        )\n",
    "        alpha = (\n",
    "            np.minimum(\n",
    "                np.minimum(\n",
    "                    np.maximum(0.001, np.random.normal(loc=alphag, scale=sd_a, size=1)),\n",
    "                    alphag + alphag,\n",
    "                ),\n",
    "                1,\n",
    "            )\n",
    "            if subjs > 1\n",
    "            else alphag\n",
    "        )\n",
    "        scaler = (\n",
    "            np.random.normal(loc=scalerg, scale=sd_v, size=1) if subjs > 1 else scalerg\n",
    "        )\n",
    "        # create a normalized regressor that is combined with the neural coefficient to create trial-by-trial values for decision threshold\n",
    "        neural_reg = np.random.normal(0, 1, size=n)\n",
    "        q_up = np.tile([q_init], n)\n",
    "        q_low = np.tile([q_init], n)\n",
    "        response = np.tile([0.5], n)\n",
    "        feedback = np.tile([0.5], n)\n",
    "        rt = np.tile([0], n)\n",
    "        rew_up = np.random.binomial(1, p_upper, n).astype(float)\n",
    "        rew_low = np.random.binomial(1, p_lower, n).astype(float)\n",
    "        sim_drift = np.tile([0], n)\n",
    "        subj_idx = np.tile([s], n)\n",
    "        d = {\n",
    "            \"q_up\": q_up,\n",
    "            \"q_low\": q_low,\n",
    "            \"sim_drift\": sim_drift,\n",
    "            \"rew_up\": rew_up,\n",
    "            \"rew_low\": rew_low,\n",
    "            \"response\": response,\n",
    "            \"rt\": rt,\n",
    "            \"feedback\": feedback,\n",
    "            \"subj_idx\": subj_idx,\n",
    "            \"split_by\": split_by,\n",
    "            \"trial\": 1,\n",
    "            \"neural_reg\": neural_reg,\n",
    "        }\n",
    "        df = pd.DataFrame(data=d)\n",
    "        df = df[\n",
    "            [\n",
    "                \"q_up\",\n",
    "                \"q_low\",\n",
    "                \"sim_drift\",\n",
    "                \"rew_up\",\n",
    "                \"rew_low\",\n",
    "                \"response\",\n",
    "                \"rt\",\n",
    "                \"feedback\",\n",
    "                \"subj_idx\",\n",
    "                \"split_by\",\n",
    "                \"trial\",\n",
    "                \"neural_reg\",\n",
    "            ]\n",
    "        ]\n",
    "        # generate data trial-by-trial using the Intercept (a), regressor (neural_reg) and coefficient (neural) for decision threshold.\n",
    "        data, params = hddm.generate.gen_rand_data(\n",
    "            {\n",
    "                \"a\": a + neural * df.loc[0, \"neural_reg\"],\n",
    "                \"t\": t,\n",
    "                \"v\": df.loc[0, \"sim_drift\"],\n",
    "                \"z\": z,\n",
    "            },\n",
    "            subjs=1,\n",
    "            size=1,\n",
    "        )\n",
    "        df.loc[0, \"response\"] = data.response[0]\n",
    "        df.loc[0, \"rt\"] = data.rt[0]\n",
    "        if data.response[0] == 1.0:\n",
    "            df.loc[0, \"feedback\"] = df.loc[0, \"rew_up\"]\n",
    "        else:\n",
    "            df.loc[0, \"feedback\"] = df.loc[0, \"rew_low\"]\n",
    "\n",
    "        for i in range(1, n):\n",
    "            df.loc[i, \"trial\"] = i + 1\n",
    "            df.loc[i, \"q_up\"] = (\n",
    "                df.loc[i - 1, \"q_up\"] * (1 - df.loc[i - 1, \"response\"])\n",
    "            ) + (\n",
    "                (df.loc[i - 1, \"response\"])\n",
    "                * (\n",
    "                    df.loc[i - 1, \"q_up\"]\n",
    "                    + (alpha * (df.loc[i - 1, \"rew_up\"] - df.loc[i - 1, \"q_up\"]))\n",
    "                )\n",
    "            )\n",
    "            df.loc[i, \"q_low\"] = (\n",
    "                df.loc[i - 1, \"q_low\"] * (df.loc[i - 1, \"response\"])\n",
    "            ) + (\n",
    "                (1 - df.loc[i - 1, \"response\"])\n",
    "                * (\n",
    "                    df.loc[i - 1, \"q_low\"]\n",
    "                    + (alpha * (df.loc[i - 1, \"rew_low\"] - df.loc[i - 1, \"q_low\"]))\n",
    "                )\n",
    "            )\n",
    "            df.loc[i, \"sim_drift\"] = (df.loc[i, \"q_up\"] - df.loc[i, \"q_low\"]) * (scaler)\n",
    "            data, params = hddm.generate.gen_rand_data(\n",
    "                {\n",
    "                    \"a\": a + neural * df.loc[i, \"neural_reg\"],\n",
    "                    \"t\": t,\n",
    "                    \"v\": df.loc[i, \"sim_drift\"],\n",
    "                    \"z\": z,\n",
    "                },\n",
    "                subjs=1,\n",
    "                size=1,\n",
    "            )\n",
    "            df.loc[i, \"response\"] = data.response[0]\n",
    "            df.loc[i, \"rt\"] = data.rt[0]\n",
    "            if data.response[0] == 1.0:\n",
    "                df.loc[i, \"feedback\"] = df.loc[i, \"rew_up\"]\n",
    "            else:\n",
    "                df.loc[i, \"feedback\"] = df.loc[i, \"rew_low\"]\n",
    "        all_data.append(df)\n",
    "    all_data = pd.concat(all_data, axis=0)\n",
    "    all_data = all_data[\n",
    "        [\n",
    "            \"q_up\",\n",
    "            \"q_low\",\n",
    "            \"sim_drift\",\n",
    "            \"response\",\n",
    "            \"rt\",\n",
    "            \"feedback\",\n",
    "            \"subj_idx\",\n",
    "            \"split_by\",\n",
    "            \"trial\",\n",
    "            \"neural_reg\",\n",
    "        ]\n",
    "    ]\n",
    "\n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>q_up</th>\n",
       "      <th>q_low</th>\n",
       "      <th>sim_drift</th>\n",
       "      <th>response</th>\n",
       "      <th>rt</th>\n",
       "      <th>feedback</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>split_by</th>\n",
       "      <th>trial</th>\n",
       "      <th>neural_reg</th>\n",
       "      <th>q_init</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.563624</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.655194</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.321482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.612025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.781290</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.762217</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.543180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.473025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.264806</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.762217</td>\n",
       "      <td>0.655194</td>\n",
       "      <td>0.221698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.667025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.298274</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.762217</td>\n",
       "      <td>0.451830</td>\n",
       "      <td>0.642964</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.409025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.229071</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       q_up     q_low  sim_drift  response        rt  feedback  subj_idx  \\\n",
       "0  0.500000  0.500000   0.000000       1.0  0.506025       1.0         0   \n",
       "1  0.655194  0.500000   0.321482       1.0  0.612025       1.0         0   \n",
       "2  0.762217  0.500000   0.543180       0.0  0.473025       1.0         0   \n",
       "3  0.762217  0.655194   0.221698       0.0  0.667025       0.0         0   \n",
       "4  0.762217  0.451830   0.642964       1.0  0.409025       1.0         0   \n",
       "\n",
       "   split_by  trial  neural_reg  q_init  \n",
       "0         0      1   -0.563624     0.5  \n",
       "1         0      2   -0.781290     0.5  \n",
       "2         0      3    1.264806     0.5  \n",
       "3         0      4    0.298274     0.5  \n",
       "4         0      5    0.229071     0.5  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create data with function defined above.\n",
    "# This will create trial-by-trial values for decision threshold (a) by adding the coefficient neural (here set to 0.2)\n",
    "# multiplied by a normalized regressor (neural_reg) to the 'Intercept' value of a (here set to 1)\n",
    "data_neural = gen_rand_reg_rlddm_data(\n",
    "    a=1,\n",
    "    t=0.3,\n",
    "    scaler=2,\n",
    "    alpha=0.2,\n",
    "    neural=0.2,\n",
    "    size=100,\n",
    "    p_upper=0.7,\n",
    "    p_lower=0.3,\n",
    "    subjs=25,\n",
    ")\n",
    "data_neural[\"q_init\"] = 0.5\n",
    "data_neural.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding these covariates:\n",
      "['a_Intercept', 'a_neural_reg']\n",
      " [-----------------100%-----------------] 1001 of 1000 complete in 687.8 sec                          mean         std       2.5q        25q        50q       75q      97.5q       mc err\n",
      "v                      1.77982    0.179364    1.44742    1.65881    1.77016    1.8981    2.13252    0.0154737\n",
      "v_std                 0.347709    0.156495   0.106553   0.223578   0.334912  0.456798   0.685135    0.0139315\n",
      "v_subj.0               1.90511    0.349163    1.32363    1.67743    1.86487    2.0913    2.72363    0.0222543\n",
      "v_subj.1               1.95427    0.331428    1.38104    1.72245    1.92206   2.15408    2.74285    0.0202816\n",
      "v_subj.2               1.61025    0.356265   0.861761    1.36741    1.63258    1.8535      2.247    0.0207686\n",
      "v_subj.3               1.74581     0.27481    1.24771    1.55051    1.72674   1.92711    2.35696    0.0147636\n",
      "v_subj.4               1.86945     0.29564    1.34704    1.65746    1.85039   2.04593    2.56522    0.0163225\n",
      "v_subj.5               1.68867    0.324564    1.01272    1.48896    1.70361   1.89581    2.27888     0.017118\n",
      "v_subj.6               1.64349    0.381525   0.802838    1.43458    1.65374   1.89287    2.36464    0.0223372\n",
      "v_subj.7               1.88742    0.373531    1.26646    1.64926    1.84455   2.10085    2.70312    0.0224872\n",
      "v_subj.8               1.66977     0.31559   0.973319    1.48736     1.6766   1.86767    2.28301     0.018848\n",
      "v_subj.9               1.85236    0.322536    1.21233    1.64689    1.83482    2.0544    2.51566    0.0195298\n",
      "v_subj.10              1.70042    0.305035    1.07015     1.4969      1.718   1.89541    2.30115    0.0191062\n",
      "v_subj.11              1.64573    0.320567   0.989453    1.44342    1.64364   1.84708    2.28433     0.017135\n",
      "v_subj.12              2.20135    0.391638     1.5728    1.89279    2.16443    2.4557    2.99248    0.0279293\n",
      "v_subj.13              1.78782    0.359303    1.06838    1.57175    1.77936   2.00966    2.50523    0.0199364\n",
      "v_subj.14              1.79168     0.39231    1.09438    1.55402    1.77386   2.02228    2.60944     0.019079\n",
      "v_subj.15              2.02136    0.333282    1.43574    1.78695    2.00095   2.22829    2.72837     0.020582\n",
      "v_subj.16               1.4873    0.448577   0.450346    1.23733    1.53178   1.77857     2.2913    0.0298906\n",
      "v_subj.17              1.30504    0.457928   0.350866   0.990748    1.35994   1.62736    2.13265    0.0339991\n",
      "v_subj.18              1.81636    0.342669    1.10572    1.60926    1.79665   2.01522    2.58298    0.0191542\n",
      "v_subj.19              1.83012    0.345574    1.20663     1.5937    1.80984   2.01437    2.65742    0.0200001\n",
      "v_subj.20              1.84845     0.29811    1.28992    1.64385    1.83143   2.02842    2.50095    0.0151424\n",
      "v_subj.21              1.85729    0.286864    1.32509    1.66318     1.8555   2.03206    2.45078     0.016736\n",
      "v_subj.22              1.70419     0.35553   0.922894    1.47883    1.70195   1.93783     2.3878    0.0203197\n",
      "v_subj.23               1.8667    0.300426     1.3111     1.6686    1.85051   2.06635    2.49491    0.0174151\n",
      "v_subj.24              1.71575    0.344087    1.09664    1.49461    1.70604   1.89517    2.49041    0.0225805\n",
      "t                     0.298188  0.00654069   0.285417    0.29368   0.298304  0.302372   0.311928  0.000294325\n",
      "t_std                0.0324054  0.00547613  0.0238951  0.0286551  0.0316829   0.03562  0.0452491  0.000244488\n",
      "t_subj.0              0.264553  0.00737852   0.248014   0.259855   0.265397  0.270072   0.276868  0.000372492\n",
      "t_subj.1              0.319455  0.00465895   0.308307   0.316886   0.320295  0.322908   0.326405   0.00025667\n",
      "t_subj.2              0.308603  0.00366833   0.300445   0.306272   0.309031  0.311368   0.314476  0.000195408\n",
      "t_subj.3              0.320222  0.00655217    0.30696   0.316495   0.320962  0.324928   0.330346   0.00036611\n",
      "t_subj.4              0.291017  0.00706534   0.273739   0.286488   0.291779   0.29605   0.302464    0.0003973\n",
      "t_subj.5              0.331855  0.00549101    0.31982   0.328528   0.332045  0.335794   0.341727  0.000307285\n",
      "t_subj.6              0.309673  0.00488315   0.298544   0.306777   0.310401  0.313162   0.317526  0.000281754\n",
      "t_subj.7              0.266329  0.00740167   0.250178   0.261793   0.267524  0.271729   0.278576  0.000349668\n",
      "t_subj.8              0.252991  0.00636148   0.239175   0.248954   0.253764   0.25731   0.264281  0.000338265\n",
      "t_subj.9              0.269198  0.00429258   0.259328   0.266839   0.269525  0.272217   0.276128  0.000216084\n",
      "t_subj.10             0.281187  0.00594503   0.268281   0.277557   0.281764  0.285363   0.291345  0.000282572\n",
      "t_subj.11             0.304542  0.00615726   0.290571   0.300891   0.305228  0.309101   0.314534  0.000316772\n",
      "t_subj.12             0.326847  0.00782971   0.308872   0.322209   0.327398  0.332118   0.340609  0.000374158\n",
      "t_subj.13             0.339351  0.00523811   0.328162   0.336044   0.339929  0.342927   0.348845  0.000268213\n",
      "t_subj.14             0.292229  0.00698643   0.277252   0.288111   0.292938  0.296992   0.303587  0.000398644\n",
      "t_subj.15             0.293218  0.00655804   0.278841   0.289397   0.293951  0.297953   0.304262  0.000398055\n",
      "t_subj.16             0.253921  0.00629306   0.239556   0.250449    0.25447  0.258524   0.264746  0.000323669\n",
      "t_subj.17              0.34338  0.00516349   0.331029    0.34034   0.344104   0.34712   0.351557  0.000272505\n",
      "t_subj.18             0.317688   0.0057973   0.303721   0.314444   0.318549  0.321736   0.326704   0.00025511\n",
      "t_subj.19             0.262735  0.00605717    0.24914   0.258909   0.263085  0.267158   0.273459  0.000334358\n",
      "t_subj.20             0.334918  0.00819456    0.31673    0.32951   0.335776  0.340519   0.349704  0.000394479\n",
      "t_subj.21             0.325387  0.00621515   0.310942   0.321801   0.325992  0.329647   0.335828  0.000314864\n",
      "t_subj.22             0.264683  0.00634221   0.250899   0.260806   0.264882  0.268988   0.276698  0.000331978\n",
      "t_subj.23             0.303186  0.00497151   0.292099   0.299921   0.303812  0.306877   0.311227  0.000282781\n",
      "t_subj.24             0.253861  0.00607589   0.241005   0.249865   0.254104  0.258405   0.264717  0.000342499\n",
      "alpha                  -1.5388     0.30262   -2.18862   -1.71608   -1.52683  -1.35151  -0.939043    0.0275414\n",
      "alpha_std             0.561637    0.254317   0.188437   0.370688   0.531378  0.710956    1.16209    0.0225401\n",
      "alpha_subj.0          -1.48915    0.471831   -2.45218   -1.78235    -1.4687   -1.1843  -0.575602    0.0263753\n",
      "alpha_subj.1         -0.727243     0.48732   -1.58799   -1.07403  -0.740631  -0.40709   0.298202    0.0342327\n",
      "alpha_subj.2          -1.57709    0.661173   -3.18151     -1.932   -1.51195  -1.15704  -0.368125    0.0362954\n",
      "alpha_subj.3          -1.38443    0.571389   -2.55393   -1.76189   -1.36466  -1.02392  -0.212549    0.0320569\n",
      "alpha_subj.4          -1.52256     0.48803    -2.5578   -1.80418   -1.49869  -1.21256  -0.540501    0.0298234\n",
      "alpha_subj.5          -1.36699    0.522855   -2.71826   -1.62961   -1.31544  -1.04203   -0.48471    0.0313527\n",
      "alpha_subj.6          -1.91881    0.705414     -3.692   -2.33493   -1.84719  -1.43209  -0.783988     0.050397\n",
      "alpha_subj.7          -1.62302    0.532136    -2.7047   -1.94615    -1.5907  -1.29245  -0.569097    0.0331962\n",
      "alpha_subj.8          -1.71126    0.590274   -3.01687   -2.05861   -1.61785  -1.30785  -0.696978    0.0380833\n",
      "alpha_subj.9          -1.52384    0.515235   -2.58804   -1.86888   -1.49462  -1.17632  -0.486591    0.0307837\n",
      "alpha_subj.10         -1.59304    0.507594   -2.74371     -1.876   -1.55411  -1.27088  -0.723159    0.0323703\n",
      "alpha_subj.11         -1.44643    0.594432   -2.71529   -1.73834   -1.43861  -1.12066  -0.250293    0.0338927\n",
      "alpha_subj.12         -1.35321     0.47287   -2.23727    -1.6461   -1.37627  -1.07615  -0.353729    0.0261803\n",
      "alpha_subj.13         -1.59176    0.582033   -2.79097     -1.903   -1.53142  -1.23027  -0.539582    0.0364847\n",
      "alpha_subj.14         -1.58047    0.602659   -2.87655   -1.93766   -1.54757  -1.19166  -0.441487     0.037198\n",
      "alpha_subj.15         -1.08406    0.465382   -1.99835   -1.38774   -1.10747 -0.781884  -0.166816    0.0248745\n",
      "alpha_subj.16         -2.02693     0.76558   -3.74247   -2.53212    -1.9009  -1.44076   -0.86809     0.056568\n",
      "alpha_subj.17         -1.78021    0.919036   -4.24421   -2.20273   -1.55682   -1.2184  -0.374563    0.0695981\n",
      "alpha_subj.18         -1.56633    0.513318   -2.69067   -1.85372   -1.53263  -1.24246   -0.64389    0.0296384\n",
      "alpha_subj.19         -1.68639    0.517942   -2.74198     -2.018   -1.66011  -1.34442  -0.656102    0.0332752\n",
      "alpha_subj.20         -1.35127     0.47716   -2.48011   -1.62904   -1.32074  -1.05337  -0.389908    0.0252333\n",
      "alpha_subj.21         -1.32246    0.580595   -2.40304   -1.67919   -1.32849  -1.02621  0.0897752    0.0314216\n",
      "alpha_subj.22         -1.83935    0.671932   -3.45931   -2.25761    -1.7196  -1.39198  -0.718557    0.0447202\n",
      "alpha_subj.23         -1.70156    0.479866   -2.79362   -1.99478    -1.6883  -1.37244  -0.862226    0.0274342\n",
      "alpha_subj.24         -1.80201    0.600622    -3.1985   -2.12676   -1.70361  -1.41517  -0.793611    0.0436462\n",
      "a_Intercept            1.01282   0.0222038   0.971476   0.996606    1.01076   1.02878    1.05927   0.00106768\n",
      "a_Intercept_std      0.0948422    0.018925  0.0639246   0.081078  0.0925205  0.105625   0.136657   0.00108924\n",
      "a_Intercept_subj.0     1.06505   0.0443915   0.981879    1.03446    1.06258   1.09386     1.1563   0.00236131\n",
      "a_Intercept_subj.1     1.01544   0.0416811   0.943411   0.986366    1.01283    1.0421    1.09932   0.00208359\n",
      "a_Intercept_subj.2    0.812145   0.0366041   0.746886   0.786296   0.810174  0.836595   0.887469     0.002127\n",
      "a_Intercept_subj.3      1.0261   0.0440427   0.946544   0.995273    1.02444   1.05272    1.12888   0.00222425\n",
      "a_Intercept_subj.4     1.02805   0.0435711    0.94799   0.997437    1.02599   1.05782    1.11425   0.00251182\n",
      "a_Intercept_subj.5    0.971314   0.0406365   0.894895   0.942531   0.971106  0.999048     1.0542   0.00219676\n",
      "a_Intercept_subj.6    0.931071   0.0385461   0.863884   0.903476   0.928429  0.955591    1.01732   0.00229643\n",
      "a_Intercept_subj.7     1.16098   0.0478498    1.07088    1.12873    1.15835   1.19485    1.25131   0.00222676\n",
      "a_Intercept_subj.8     1.03296   0.0408615   0.956804    1.00625    1.03054   1.05996    1.11376   0.00192685\n",
      "a_Intercept_subj.9    0.959566   0.0398964   0.886919   0.933076    0.95861  0.983758    1.04295    0.0018792\n",
      "a_Intercept_subj.10    1.00909   0.0424419   0.934106   0.977941    1.00622   1.03894     1.0935   0.00202778\n",
      "a_Intercept_subj.11     1.0881   0.0444363    1.00907    1.05667    1.08355   1.11832     1.1818   0.00213301\n",
      "a_Intercept_subj.12    1.08052   0.0455087   0.989387    1.04928    1.08072   1.11129    1.17353   0.00237405\n",
      "a_Intercept_subj.13   0.890781   0.0396113   0.817139   0.862529   0.890319  0.918796   0.969185    0.0021704\n",
      "a_Intercept_subj.14    1.03764   0.0429662   0.962838    1.00603    1.03661   1.06403    1.12337   0.00240711\n",
      "a_Intercept_subj.15   0.973689   0.0454957   0.894411   0.941504   0.970952   1.00073    1.07563   0.00257554\n",
      "a_Intercept_subj.16    1.01024   0.0407784   0.932428   0.983057    1.00868   1.03768    1.09707   0.00183196\n",
      "a_Intercept_subj.17   0.910582   0.0381291   0.837526   0.884812   0.909782  0.934532   0.988974   0.00201244\n",
      "a_Intercept_subj.18   0.962727   0.0429818   0.883188   0.931343   0.962836   0.99014     1.0526   0.00201699\n",
      "a_Intercept_subj.19   0.957915   0.0403836   0.880067   0.931306   0.957415  0.982781    1.04252   0.00208297\n",
      "a_Intercept_subj.20    1.13501   0.0480892    1.04304    1.10407    1.13162   1.16588    1.23176   0.00257405\n",
      "a_Intercept_subj.21    1.06483   0.0429713   0.979812    1.03554    1.06315   1.09358    1.15422   0.00230302\n",
      "a_Intercept_subj.22    1.05345    0.043446   0.969941    1.02381    1.05374   1.08216    1.13855   0.00240664\n",
      "a_Intercept_subj.23    1.00161   0.0403246   0.927866     0.9747   0.999837   1.02992    1.07995   0.00223534\n",
      "a_Intercept_subj.24    1.12539   0.0433619    1.04065    1.09665    1.12353   1.15507    1.21458   0.00249173\n",
      "a_neural_reg          0.192445  0.00688622   0.178638   0.188456   0.192657  0.197062   0.205718  0.000300237\n",
      "DIC: 585.060365\n",
      "deviance: 524.769182\n",
      "pD: 60.291183\n"
     ]
    }
   ],
   "source": [
    "# run a regressor model estimating the impact of 'neural' on decision threshold a. This should estimate the coefficient a_neural_reg to be 0.2\n",
    "# to run the HDDMrlRegressor you need to include alpha\n",
    "m_reg = hddm.HDDMrlRegressor(\n",
    "    data_neural, \"a ~ neural_reg\", include=[\"v\", \"a\", \"t\", \"alpha\"]\n",
    ")\n",
    "m_reg.sample(1000, burn=250)\n",
    "m_reg.print_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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